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Data X:
2 1 22 15 16 17 10 1 2 22 23 24 42 9 1 2 22 26 22 39 30 1 2 23 19 21 22 18 1 2 21 19 23 20 16 1 2 21 16 23 31 20 1 1 24 23 21 42 20 2 1 22 22 20 30 18 1 2 21 19 22 33 21 1 2 23 24 20 29 20 1 1 20 19 12 31 20 2 1 23 25 23 39 20 1 1 20 23 23 44 29 1 2 21 31 30 40 14 2 1 22 29 22 42 25 2 2 22 18 21 28 19 2 2 21 17 21 29 19 1 1 20 22 15 35 25 1 1 21 21 22 26 25 1 2 21 24 24 42 19 1 1 20 22 23 26 19 1 1 21 16 15 30 18 1 2 23 22 24 28 24 1 1 23 21 24 24 18 2 1 21 25 21 26 26 1 1 22 22 21 39 26 2 1 20 24 18 33 24 2 1 23 21 20 50 29 2 1 21 25 19 40 26 1 1 21 29 29 49 28 2 1 23 19 20 31 18 2 1 23 29 23 37 19 2 2 22 25 24 29 21 1 1 21 19 27 37 13 1 1 NA 27 28 16 19 1 2 21 25 24 28 26 1 2 21 23 29 29 17 1 1 22 24 24 31 19 2 2 22 23 22 34 28 1 2 22 25 25 30 15 1 1 22 26 24 31 16 1 1 23 23 14 44 18 2 1 NA 22 22 35 25 1 2 22 32 24 47 15 1 2 21 22 24 39 24 1 1 23 18 24 34 24 2 1 21 19 24 15 14 2 2 32 23 22 26 19 2 2 32 24 22 25 20 2 1 21 19 21 30 27 1 1 20 16 21 25 20 1 1 21 23 21 33 25 1 1 22 17 15 39 16 1 1 21 17 26 24 19 1 2 21 28 22 44 15 1 2 21 24 24 31 17 1 1 22 21 13 30 22 1 1 21 14 19 21 19 2 1 25 21 10 38 44 1 2 22 20 28 30 19 1 1 21 25 25 31 19 2 2 21 20 24 32 23 1 1 20 17 22 34 19 1 2 21 26 30 43 28 1 2 22 17 22 29 17 4 2 21 17 24 40 22 1 2 23 24 23 31 25 1 2 24 30 20 36 44 2 1 20 25 22 39 19 2 1 21 15 22 31 21 1 1 23 25 19 36 25 1 1 24 18 24 36 28 1 1 22 20 22 36 19 1 1 22 32 26 41 33 2 2 21 14 12 23 19 2 1 20 20 25 23 12 1 2 21 25 29 34 15 1 NA 21 25 23 31 23 1 1 22 25 23 26 21 1 1 21 35 17 32 29 1 2 21 29 26 48 15 1 1 22 25 27 37 23 1 2 23 21 23 31 25 1 1 23 21 20 29 18 1 1 21 24 24 44 24 1 1 22 26 22 27 20 1 1 24 24 26 31 19 1 2 21 20 29 37 21 1 1 25 24 20 31 33 2 1 24 18 17 29 17 1 1 21 17 16 25 19 1 2 21 22 24 39 19 1 1 22 22 24 33 23 1 1 22 22 19 26 21 2 1 21 24 29 39 20 1 1 24 32 25 33 19 1 2 21 19 25 22 16 1 2 21 21 24 27 20 1 2 22 23 29 31 18 2 1 22 26 22 40 11 1 2 21 18 23 45 18 2 1 21 19 15 33 23 1 1 22 22 29 26 19 1 2 21 27 21 28 21 2 1 21 21 23 28 16 2 1 22 20 20 30 24 2 2 21 21 25 47 23 1 1 21 20 28 30 20 1 1 21 29 18 37 23 1 1 23 30 25 23 25 1 1 22 23 24 24 24 2 1 21 29 23 15 19 1 2 22 19 25 39 15 1 1 22 26 27 32 20 1 1 22 22 24 23 14 2 1 23 26 24 41 34 2 1 24 27 26 39 24 2 1 22 19 18 42 26 2 2 21 24 26 38 23 1 2 21 26 23 30 18 2 2 22 22 28 19 11 1 1 20 23 20 45 28 1 1 23 25 23 36 15 1 2 21 19 24 36 26 1 1 22 20 21 33 20 2 1 23 25 25 35 21 2 1 21 14 16 29 21 1 2 21 20 19 28 15 1 1 23 27 22 40 30 1 2 21 21 27 28 21 1 2 21 21 24 36 12 1 2 21 14 17 28 23 2 1 23 21 21 42 30 1 1 21 23 21 27 22 2 1 22 18 19 32 21 1 2 22 20 25 27 19 1 2 21 19 24 30 22 1 2 21 15 21 31 18 1 2 24 23 26 26 16 1 1 21 26 25 47 19 1 1 21 21 25 28 20 1 2 20 13 13 31 12 2 2 21 24 25 26 21 1 2 NA 17 23 27 14 1 1 24 21 26 28 23 1 1 22 28 22 44 30 1 1 22 22 20 32 21 1 1 22 25 14 25 25 1 1 22 18 23 24 13 1 1 23 27 24 45 17 1 1 22 25 21 37 24 2 1 22 21 24 25 21
Names of X columns:
Roken Geslacht Leeftijd PS O CMD PEC
Type of Correlation
kendall
pearson
spearman
kendall
Chart options
Title:
R Code
panel.tau <- function(x, y, digits=2, prefix='', cex.cor) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) rr <- cor.test(x, y, method=par1) r <- round(rr$p.value,2) txt <- format(c(r, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep='') if(missing(cex.cor)) cex <- 0.5/strwidth(txt) text(0.5, 0.5, txt, cex = cex) } panel.hist <- function(x, ...) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'pair',1,TRUE) a<-table.element(a,'Pearson r',1,TRUE) a<-table.element(a,'Spearman rho',1,TRUE) a<-table.element(a,'Kendall tau',1,TRUE) a<-table.row.end(a) cor.test(y[1,],y[2,],method=par1) for (i in 1:(n-1)) { for (j in (i+1):n) { a<-table.row.start(a) dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='') a<-table.element(a,dum,header=TRUE) rp <- cor.test(y[i,],y[j,],method='pearson') a<-table.element(a,round(rp$estimate,4)) rs <- cor.test(y[i,],y[j,],method='spearman') a<-table.element(a,round(rs$estimate,4)) rk <- cor.test(y[i,],y[j,],method='kendall') a<-table.element(a,round(rk$estimate,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=T) a<-table.element(a,paste('(',round(rp$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rs$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rk$p.value,4),')',sep='')) a<-table.row.end(a) } } a<-table.end(a) table.save(a,file='mytable1.tab')
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